# Lint as: python3
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Dataclasses for optimizer configs."""
from typing import List, Optional

import dataclasses
from official.modeling.hyperparams import base_config


@dataclasses.dataclass
class SGDConfig(base_config.Config):
  """Configuration for SGD optimizer.

  The attributes for this class matches the arguments of tf.keras.optimizer.SGD.

  Attributes:
    name: name of the optimizer.
    learning_rate: learning_rate for SGD optimizer.
    decay: decay rate for SGD optimizer.
    nesterov: nesterov for SGD optimizer.
    momentum: momentum for SGD optimizer.
  """
  name: str = "SGD"
  learning_rate: float = 0.01
  decay: float = 0.0
  nesterov: bool = False
  momentum: float = 0.0


@dataclasses.dataclass
class AdamConfig(base_config.Config):
  """Configuration for Adam optimizer.

  The attributes for this class matches the arguments of
  tf.keras.optimizer.Adam.

  Attributes:
    name: name of the optimizer.
    learning_rate: learning_rate for Adam optimizer.
    beta_1: decay rate for 1st order moments.
    beta_2: decay rate for 2st order moments.
    epsilon: epsilon value used for numerical stability in Adam optimizer.
    amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from
    the paper "On the Convergence of Adam and beyond".
  """
  name: str = "Adam"
  learning_rate: float = 0.001
  beta_1: float = 0.9
  beta_2: float = 0.999
  epsilon: float = 1e-07
  amsgrad: bool = False


@dataclasses.dataclass
class AdamWeightDecayConfig(base_config.Config):
  """Configuration for Adam optimizer with weight decay.

  Attributes:
    name: name of the optimizer.
    learning_rate: learning_rate for the optimizer.
    beta_1: decay rate for 1st order moments.
    beta_2: decay rate for 2st order moments.
    epsilon: epsilon value used for numerical stability in the optimizer.
    amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from
    the paper "On the Convergence of Adam and beyond".
    weight_decay_rate: float. Weight decay rate. Default to 0.
    include_in_weight_decay: list[str], or None. List of weight names to include
                             in weight decay.
    include_in_weight_decay: list[str], or None. List of weight names to not
                             include in weight decay.
  """
  name: str = "AdamWeightDecay"
  learning_rate: float = 0.001
  beta_1: float = 0.9
  beta_2: float = 0.999
  epsilon: float = 1e-07
  amsgrad: bool = False
  weight_decay_rate: float = 0.0
  include_in_weight_decay: Optional[List[str]] = None
  exclude_from_weight_decay: Optional[List[str]] = None


@dataclasses.dataclass
class LAMBConfig(base_config.Config):
  """Configuration for LAMB optimizer.

  The attributes for this class matches the arguments of
  tensorflow_addons.optimizers.LAMB.

  Attributes:
    name: name of the optimizer.
    learning_rate: learning_rate for Adam optimizer.
    beta_1: decay rate for 1st order moments.
    beta_2: decay rate for 2st order moments.
    epsilon: epsilon value used for numerical stability in LAMB optimizer.
    weight_decay_rate: float. Weight decay rate. Default to 0.
    exclude_from_weight_decay: List of regex patterns of variables excluded from
                               weight decay. Variables whose name contain a
                               substring matching the pattern will be excluded.
    exclude_from_layer_adaptation: List of regex patterns of variables excluded
                                   from layer adaptation. Variables whose name
                                   contain a substring matching the pattern will
                                   be excluded.
  """
  name: str = "LAMB"
  learning_rate: float = 0.001
  beta_1: float = 0.9
  beta_2: float = 0.999
  epsilon: float = 1e-6
  weight_decay_rate: float = 0.0
  exclude_from_weight_decay: Optional[List[str]] = None
  exclude_from_layer_adaptation: Optional[List[str]] = None
